TNN: Tree Neural Network for Airway Anatomical Labeling.

Abstract

Detailed anatomical labeling of bronchial trees extracted from CT images can be used as fine-grained maps for intra-operative navigation. To cater to the sparse distribution of airway voxels and large class imbalance in 3D image space, a graph-neural-network-based method is proposed to map branches to nodes in a graph space and assign anatomical labels down to subsegmental level. To address the inherent problem of overlapping distribution of positional and morphological features, especially for subsegmental categories, the proposed method focuses on the relative position between sibling subsegments which is fixed in most cases. The hierarchical nomenclature is represented by multi-level labeling and each category is associated with one or two subtrees in the graph. Hyperedges are used to extract the representation of subtrees while a hypergraph neural network is developed to encode their intrinsic relationship through hyperedge interaction. A filter module is further designed to guide feature aggregation between nodes and hyperedges. With the proposed method, the final accuracies for segmental and subsegmental node classification can achieve 93.6% and 82.0% respectively. The corresponding code is publicly available at https://github.com/haozheng-sjtu/airway-labeling.